Chicago Crime Data Exploration¶
The data covers daily crimes in Chicago in 2024. It's updated daily. Here is the link to the data: https://data.cityofchicago.org/Public-Safety/Crimes-2024/dqcy-ctma/about_data
In [3]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [11]:
# Load the data
file_path = '/Users/YigitAydede/Library/CloudStorage/Dropbox/Documents/Courses/MBAN/NLPBootcamp/PythonBC/Crimes_-_2024_20240804.csv'
data = pd.read_csv(file_path)
df = pd.DataFrame(data)
# You can now work with the data variable which contains the contents of the CSV file
1. Let's see the data¶
In [12]:
# Statistical summary of the data
df.describe()
# Display the first 5 rows of the data
print(df.head())
# Display the last 5 rows of the data
print(df.tail())
# Display the shape of the data
print(df.shape)
# Display the column names
print(df.columns)
# Display the data types of the columns
print(df.dtypes)
# Display information about the data
print(df.info())
ID Case Number Date Block \
0 13543719 JH364008 07/27/2024 12:00:00 AM 018XX S SPRINGFIELD AVE
1 13551073 JH372867 07/27/2024 12:00:00 AM 016XX E 68TH ST
2 13548638 JH369010 07/27/2024 12:00:00 AM 034XX N CLARK ST
3 13546333 JH367190 07/27/2024 12:00:00 AM 006XX E 90TH ST
4 13544042 JH364428 07/27/2024 12:00:00 AM 001XX W HUBBARD ST
IUCR Primary Type Description Location Description \
0 0910 MOTOR VEHICLE THEFT AUTOMOBILE STREET
1 1130 DECEPTIVE PRACTICE FRAUD OR CONFIDENCE GAME APARTMENT
2 0890 THEFT FROM BUILDING BAR OR TAVERN
3 0810 THEFT OVER $500 RESIDENCE
4 0870 THEFT POCKET-PICKING BAR OR TAVERN
Arrest Domestic ... Ward Community Area FBI Code X Coordinate \
0 False False ... 24 29 07 1150693.0
1 False False ... 5 43 11 NaN
2 False False ... 44 6 06 1168850.0
3 False False ... 8 44 06 1181979.0
4 False False ... 42 8 06 1175290.0
Y Coordinate Year Updated On Latitude Longitude \
0 1890610.0 2024 08/03/2024 03:40:46 PM 41.855729 -87.722368
1 NaN 2024 08/03/2024 03:40:46 PM NaN NaN
2 1923293.0 2024 08/03/2024 03:40:46 PM 41.945040 -87.654775
3 1845440.0 2024 08/03/2024 03:40:46 PM 41.731111 -87.608932
4 1903292.0 2024 08/03/2024 03:40:46 PM 41.890014 -87.631705
Location
0 POINT (-87.722368377 41.855729314)
1 NaN
2 POINT (-87.654774622 41.945039683)
3 POINT (-87.608931577 41.731110605)
4 POINT (-87.631705393 41.890013771)
[5 rows x 22 columns]
ID Case Number Date \
144920 13368627 JH152302 01/01/2024 12:00:00 AM
144921 13368833 JH152568 01/01/2024 12:00:00 AM
144922 13369774 JH153703 01/01/2024 12:00:00 AM
144923 13369425 JH153114 01/01/2024 12:00:00 AM
144924 13325302 JH100531 01/01/2024 12:00:00 AM
Block IUCR Primary Type \
144920 028XX E 77TH ST 1153 DECEPTIVE PRACTICE
144921 058XX S DR MARTIN LUTHER KING JR DR 1540 OBSCENITY
144922 047XX W MAYPOLE AVE 2820 OTHER OFFENSE
144923 008XX W ERIE ST 0820 THEFT
144924 038XX N DRAKE AVE 0850 THEFT
Description \
144920 FINANCIAL IDENTITY THEFT OVER $ 300
144921 OBSCENE MATTER
144922 TELEPHONE THREAT
144923 $500 AND UNDER
144924 ATTEMPT THEFT
Location Description Arrest Domestic ... Ward \
144920 OTHER (SPECIFY) False False ... 7
144921 APARTMENT False True ... 20
144922 APARTMENT False True ... 28
144923 PARKING LOT / GARAGE (NON RESIDENTIAL) False False ... 27
144924 STREET False False ... 35
Community Area FBI Code X Coordinate Y Coordinate Year \
144920 43 11 1196407.0 1854530.0 2024
144921 40 26 1179922.0 1866345.0 2024
144922 25 08A 1144822.0 1901044.0 2024
144923 24 06 1170456.0 1904469.0 2024
144924 16 06 1152036.0 1925453.0 2024
Updated On Latitude Longitude \
144920 02/16/2024 03:40:38 PM 41.755709 -87.555777
144921 05/02/2024 03:41:47 PM 41.788523 -87.615828
144922 02/17/2024 03:40:44 PM 41.884474 -87.743655
144923 02/17/2024 03:40:44 PM 41.893351 -87.649423
144924 01/08/2024 03:59:56 PM 41.951316 -87.716519
Location
144920 POINT (-87.555776662 41.755708907)
144921 POINT (-87.615828297 41.788523444)
144922 POINT (-87.74365494 41.884474129)
144923 POINT (-87.649423417 41.893350627)
144924 POINT (-87.716519304 41.951315505)
[5 rows x 22 columns]
(144925, 22)
Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',
'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',
'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',
'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',
'Location'],
dtype='object')
ID int64
Case Number object
Date object
Block object
IUCR object
Primary Type object
Description object
Location Description object
Arrest bool
Domestic bool
Beat int64
District int64
Ward int64
Community Area int64
FBI Code object
X Coordinate float64
Y Coordinate float64
Year int64
Updated On object
Latitude float64
Longitude float64
Location object
dtype: object
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 144925 entries, 0 to 144924
Data columns (total 22 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ID 144925 non-null int64
1 Case Number 144925 non-null object
2 Date 144925 non-null object
3 Block 144925 non-null object
4 IUCR 144925 non-null object
5 Primary Type 144925 non-null object
6 Description 144925 non-null object
7 Location Description 144335 non-null object
8 Arrest 144925 non-null bool
9 Domestic 144925 non-null bool
10 Beat 144925 non-null int64
11 District 144925 non-null int64
12 Ward 144925 non-null int64
13 Community Area 144925 non-null int64
14 FBI Code 144925 non-null object
15 X Coordinate 144826 non-null float64
16 Y Coordinate 144826 non-null float64
17 Year 144925 non-null int64
18 Updated On 144925 non-null object
19 Latitude 144826 non-null float64
20 Longitude 144826 non-null float64
21 Location 144826 non-null object
dtypes: bool(2), float64(4), int64(6), object(10)
memory usage: 22.4+ MB
None
2. Selecting columns and rows¶
In [13]:
print("\nSelected columns:")
selected_columns = ['ID', 'Primary Type', 'Description', 'Location Description', 'Arrest', 'Domestic']
print(df[selected_columns].head())
Selected columns:
ID Primary Type Description \
0 13543719 MOTOR VEHICLE THEFT AUTOMOBILE
1 13551073 DECEPTIVE PRACTICE FRAUD OR CONFIDENCE GAME
2 13548638 THEFT FROM BUILDING
3 13546333 THEFT OVER $500
4 13544042 THEFT POCKET-PICKING
Location Description Arrest Domestic
0 STREET False False
1 APARTMENT False False
2 BAR OR TAVERN False False
3 RESIDENCE False False
4 BAR OR TAVERN False False
In [14]:
print("\nSelected rows based on condition (e.g., Arrest == True):")
arrested_crimes = df[df['Arrest'] == True]
print(arrested_crimes.head())
Selected rows based on condition (e.g., Arrest == True):
ID Case Number Date Block \
20 13543641 JH363989 07/27/2024 12:00:00 AM 053XX N BROADWAY
23 13543156 JH363451 07/26/2024 11:58:00 PM 009XX W LAKE ST
27 13543227 JH363420 07/26/2024 11:51:00 PM 003XX E 75TH ST
28 13543125 JH363403 07/26/2024 11:51:00 PM 006XX S SPRINGFIELD AVE
31 13543260 JH363408 07/26/2024 11:46:00 PM 034XX W BELMONT AVE
IUCR Primary Type Description \
20 0460 BATTERY SIMPLE
23 0470 PUBLIC PEACE VIOLATION RECKLESS CONDUCT
27 143A WEAPONS VIOLATION UNLAWFUL POSSESSION - HANDGUN
28 2024 NARCOTICS POSSESS - HEROIN (WHITE)
31 0860 THEFT RETAIL THEFT
Location Description Arrest Domestic ... Ward Community Area \
20 GROCERY FOOD STORE True False ... 48 77
23 SIDEWALK True False ... 27 28
27 STREET True False ... 6 69
28 STREET True False ... 24 26
31 DRUG STORE True False ... 35 21
FBI Code X Coordinate Y Coordinate Year Updated On \
20 08B 1167347.0 1935654.0 2024 08/03/2024 03:40:46 PM
23 24 1169954.0 1901640.0 2024 08/03/2024 03:40:46 PM
27 15 1179508.0 1855350.0 2024 08/03/2024 03:40:46 PM
28 18 1150484.0 1896982.0 2024 08/03/2024 03:40:46 PM
31 06 1152928.0 1921067.0 2024 08/03/2024 03:40:46 PM
Latitude Longitude Location
20 41.978991 -87.659942 POINT (-87.659941972 41.978991238)
23 41.885599 -87.651350 POINT (-87.651349647 41.885598628)
27 41.758362 -87.617682 POINT (-87.617681742 41.758361509)
28 41.873219 -87.722969 POINT (-87.72296923 41.873218908)
31 41.939262 -87.713357 POINT (-87.71335694 41.939262335)
[5 rows x 22 columns]
3. Missing values¶
In [16]:
print("\nMissing values in each column:")
print(df.isnull().sum())
Missing values in each column: ID 0 Case Number 0 Date 0 Block 0 IUCR 0 Primary Type 0 Description 0 Location Description 590 Arrest 0 Domestic 0 Beat 0 District 0 Ward 0 Community Area 0 FBI Code 0 X Coordinate 99 Y Coordinate 99 Year 0 Updated On 0 Latitude 99 Longitude 99 Location 99 dtype: int64
In [15]:
# Visualize missing values
plt.figure(figsize=(12, 6))
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis')
plt.title('Missing Value Heatmap')
plt.show()
4. Explore the data¶
In [17]:
# Analyzes crime type distribution with a bar plot of the top 10 crime types
print("\nCrime type distribution:")
crime_type_counts = df['Primary Type'].value_counts()
print(crime_type_counts)
plt.figure(figsize=(12, 6))
crime_type_counts[:10].plot(kind='bar')
plt.title('Top 10 Crime Types')
plt.xlabel('Crime Type')
plt.ylabel('Count')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
Crime type distribution: Primary Type THEFT 33076 BATTERY 26285 CRIMINAL DAMAGE 16107 ASSAULT 13363 MOTOR VEHICLE THEFT 12620 OTHER OFFENSE 9719 DECEPTIVE PRACTICE 8499 ROBBERY 5213 WEAPONS VIOLATION 4768 BURGLARY 4282 NARCOTICS 3318 CRIMINAL TRESPASS 2732 OFFENSE INVOLVING CHILDREN 1019 CRIMINAL SEXUAL ASSAULT 840 SEX OFFENSE 716 PUBLIC PEACE VIOLATION 553 INTERFERENCE WITH PUBLIC OFFICER 388 HOMICIDE 335 ARSON 273 STALKING 256 PROSTITUTION 152 LIQUOR LAW VIOLATION 112 CONCEALED CARRY LICENSE VIOLATION 110 INTIMIDATION 86 KIDNAPPING 51 OBSCENITY 33 GAMBLING 10 PUBLIC INDECENCY 4 OTHER NARCOTIC VIOLATION 2 HUMAN TRAFFICKING 2 NON-CRIMINAL 1 Name: count, dtype: int64
In [18]:
# Calculates and visualizes the arrest rate
print("\nArrest rate:")
arrest_rate = df['Arrest'].value_counts(normalize=True)
print(arrest_rate)
plt.figure(figsize=(8, 6))
arrest_rate.plot(kind='pie', autopct='%1.1f%%')
plt.title('Arrest Rate')
plt.ylabel('')
plt.show()
Arrest rate: Arrest False 0.86778 True 0.13222 Name: proportion, dtype: float64
In [20]:
# Calculate arrest rates by crime types
arrest_rates = df.groupby('Primary Type')['Arrest'].mean()
# Display the arrest rates
print(arrest_rates.sort_values(ascending=False))
Primary Type GAMBLING 1.000000 PUBLIC INDECENCY 1.000000 OTHER NARCOTIC VIOLATION 1.000000 CONCEALED CARRY LICENSE VIOLATION 0.963636 NARCOTICS 0.946956 LIQUOR LAW VIOLATION 0.946429 PROSTITUTION 0.940789 INTERFERENCE WITH PUBLIC OFFICER 0.878866 WEAPONS VIOLATION 0.584312 PUBLIC PEACE VIOLATION 0.520796 OBSCENITY 0.424242 CRIMINAL TRESPASS 0.286237 HOMICIDE 0.194030 OTHER OFFENSE 0.191069 BATTERY 0.163477 ASSAULT 0.101923 ARSON 0.076923 SEX OFFENSE 0.071229 STALKING 0.070312 OFFENSE INVOLVING CHILDREN 0.062807 THEFT 0.061223 ROBBERY 0.054096 BURGLARY 0.040168 CRIMINAL DAMAGE 0.034705 DECEPTIVE PRACTICE 0.032945 MOTOR VEHICLE THEFT 0.029002 CRIMINAL SEXUAL ASSAULT 0.020238 KIDNAPPING 0.019608 INTIMIDATION 0.011628 NON-CRIMINAL 0.000000 HUMAN TRAFFICKING 0.000000 Name: Arrest, dtype: float64
In [26]:
import datetime
# Ensure the 'Date' column is in datetime format
df['Date'] = pd.to_datetime(df['Date'])
# Filter the data for 'ROBBERY'
robbery_data = df[df['Primary Type'] == 'ROBBERY']
# Extract the day of the week
robbery_data['Day of Week'] = robbery_data['Date'].dt.day_name()
# Group the data by day of the week and count the number of robberies
robbery_by_day = robbery_data.groupby('Day of Week').size()
# Reindex to ensure all days are represented
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
robbery_by_day = robbery_by_day.reindex(days_of_week, fill_value=0)
# Create the bar plot
plt.figure(figsize=(10, 6))
robbery_by_day.plot(kind='bar')
plt.title('Robbery by Day of the Week')
plt.xlabel('Day of the Week')
plt.ylabel('Number of Robberies')
plt.xticks(rotation=45)
plt.show()
/var/folders/b2/gpnsjh9j6bv5prtx7w5lsym80000gp/T/ipykernel_84591/3956560368.py:10: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy robbery_data['Day of Week'] = robbery_data['Date'].dt.day_name()
5. Mapping the 2024 Chicago Crime Data¶
In [2]:
%%capture
!pip install folium
In [29]:
import folium
from folium.plugins import HeatMap
# Create a map centered around Chicago
chicago_map = folium.Map(location=[41.8781, -87.6298], zoom_start=10)
# Create a HeatMap layer using the crime data
heat_data = df[['Latitude', 'Longitude']].dropna()
heat_map = HeatMap(data=heat_data, radius=15)
# Add the HeatMap layer to the map
heat_map.add_to(chicago_map)
# Display the map
chicago_map
Out[29]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [30]:
# 6. Geographical Distribution
plt.figure(figsize=(12, 8))
plt.scatter(df['Longitude'], df['Latitude'], alpha=0.1)
plt.title('Geographical Distribution of Crimes')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()
6. Time-based Analysis¶
Extracts the hour from the date and analyzes hourly crime distribution
In [40]:
file_path = '/Users/YigitAydede/Library/CloudStorage/Dropbox/Documents/Courses/MBAN/NLPBootcamp/PythonBC/Crimes_-_2024_20240804.csv'
df = pd.read_csv(file_path, parse_dates=['Date'])
# Ensure the 'Date' column is in datetime format with AM/PM
df['Date'] = pd.to_datetime(df['Date'], format='%m/%d/%Y %I:%M:%S %p', errors='coerce')
# Extract the hour from the 'Date' column
df['Hour'] = df['Date'].dt.hour
# Create a histogram of the crime distribution in 24 hours
plt.figure(figsize=(12, 6))
plt.hist(df['Hour'], bins=24, edgecolor='black')
plt.title('Distribution of Crime in 24 Hours')
plt.xlabel('Hour')
plt.ylabel('Count')
plt.xticks(range(24))
plt.show()
/var/folders/b2/gpnsjh9j6bv5prtx7w5lsym80000gp/T/ipykernel_84591/1865543446.py:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. df = pd.read_csv(file_path, parse_dates=['Date'])
In [42]:
import numpy as np
### Each crime type in 24 hours
# Get unique crime types
crime_types = df['Primary Type'].unique()
# Determine the number of rows and columns for the subplots
num_crime_types = len(crime_types)
num_cols = 3
num_rows = int(np.ceil(num_crime_types / num_cols))
# Create subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 5 * num_rows), constrained_layout=True)
# Plot histograms for each crime type
for i, crime_type in enumerate(crime_types):
row = i // num_cols
col = i % num_cols
ax = axes[row, col] if num_rows > 1 else axes[col]
# Filter data for the current crime type
crime_data = df[df['Primary Type'] == crime_type]
# Create histogram
ax.hist(crime_data['Hour'], bins=24, edgecolor='black')
ax.set_title(f'Distribution of {crime_type} in 24 Hours')
ax.set_xlabel('Hour')
ax.set_ylabel('Count')
ax.set_xticks(range(24))
# Remove any empty subplots
for j in range(i + 1, num_rows * num_cols):
row = j // num_cols
col = j % num_cols
fig.delaxes(axes[row, col] if num_rows > 1 else axes[col])
# Display the plots
plt.show()
